奇异值分解变化结果

Question

我尝试使用svds执行文本摘要，但是每次运行该函数时，摘要结果都会发生变化。有人能告诉我原因和解决办法吗？ 我甚至检查了独立阵列u、s和v，即使它们每次运行后都在变化。如何使它们静止？ 在svds编码之后，句子矩阵的计算如下。数据集是来自澳大利亚最高法院的一些法律文件

def _compute_matrix(sentences, weighting, norm):
    if weighting.lower() == 'binary':
        vectorizer = CountVectorizer(min_df=1, ngram_range=(1, 1), 
        binary=True, stop_words=None)
    elif weighting.lower() == 'frequency':
        vectorizer = CountVectorizer(min_df=1, ngram_range=(1, 1), 
        binary=False, stop_words=None)
    elif weighting.lower() == 'tfidf':
        vectorizer = TfidfVectorizer(min_df=1, ngram_range=(1, 1), 
        stop_words=None)
    else:
        raise ValueError('Parameter "method" must take one of the values 
        "binary", "frequency" or "tfidf".')

    # Extract word features from sentences using sparse vectorizer
    frequency_matrix = vectorizer.fit_transform(sentences).astype(float)

    terms = vectorizer.get_feature_names()

    if norm in ('l1', 'l2'):
        frequency_matrix = normalize(frequency_matrix, norm=norm, axis=1)
    elif norm is not None:
        raise ValueError('Parameter "norm" can only take values "l1", "l2" 
        or None')

    return frequency_matrix, terms

processed_sentences = _createsentences(raw_content)
sentence_matrix, feature_names = _compute_matrix(processed_sentences, 
weighting='tfidf', norm='l2')
sentence_matrix = sentence_matrix.transpose()
sentence_matrix = sentence_matrix.multiply(sentence_matrix > 0)
print(sentence_matrix.shape)

u, s, v = svds(sentence_matrix, k=20)
topic_sigma_threshold = 0.5
topic_averages = v.mean(axis=1)

for topic_ndx, topic_avg in enumerate(topic_averages):
    v[topic_ndx, v[topic_ndx, :] <= topic_avg] = 0

if 1 <= topic_sigma_threshold < 0:
   raise ValueError('Parameter topic_sigma_threshold must take a value 
   between 0 and 1')

sigma_threshold = max(s) * topic_sigma_threshold
s[s < sigma_threshold] = 0  

saliency_vec = np.dot(np.square(s), np.square(v))

top_sentences = saliency_vec.argsort()[-25:][::-1]
top_sentences.sort()

[processed_sentences[i] for i in top_sentences]

Answer 1

我通过玩svd的参数和理解svd的源代码找到了解决方案。奇异值分解采用稀疏矩阵维数N的随机初始向量。因此，要将初始向量设置为常量，我们必须使用v0参数，代码如下所述

np.random.seed(0)
v0 = np.random.rand(min(sentence_matrix.shape))

u, s, v = svds(sentence_matrix, k=20, v0=v0)